library(glmmTMB)
library(emmeans)
library(DHARMa)
library(car)
library(lme4)
library(performance)
library(dplyr)
library(coxme)
library(ggplot2)
## Analysis split for red and orange
data_complete <- readRDS("../output/data_complete.rds")
data_complete_SP <- readRDS("../output/data_complete_SP.rds")# Modèle Orange concentration and inf 20 time lost
data_orange_inf20 <- data_complete %>% dplyr::filter(Experiment=="Orange"&Prop_time_lost<0.2)
data_orange_SP <- data_complete_SP %>% dplyr::filter(Experiment=="Orange" & Prop_time_lost<0.2)### Prop time moving
ggplot(data_orange_inf20)+
geom_boxplot(aes(x=Traitement, y=Prop_time_moving, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Prop_time_moving, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
modPTM <- glmmTMB(sqrt(Prop_time_moving)~Traitement+sexe+(1|Replicat),
data=data_orange_inf20)
res <- simulateResiduals(modPTM)
plot(res)
summary(modPTM) Family: gaussian ( identity )
Formula: sqrt(Prop_time_moving) ~ Traitement + sexe + (1 | Replicat)
Data: data_orange_inf20
AIC BIC logLik deviance df.resid
-400.4 -384.0 205.2 -410.4 190
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Replicat (Intercept) 0.0005099 0.02258
Residual 0.0068948 0.08304
Number of obs: 195, groups: Replicat, 5
Dispersion estimate for gaussian family (sigma^2): 0.00689
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.5210535 0.0141468 36.83 <2e-16 ***
TraitementPermethrin_orange -0.0009606 0.0119528 -0.08 0.936
sexeM 0.0180279 0.0120840 1.49 0.136
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(modPTM, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe response SE df lower.CL upper.CL t.ratio p.value
Control F 0.271 0.0147 190 0.243 0.301 36.832 <.0001
Permethrin_orange F 0.270 0.0152 190 0.241 0.301 35.624 <.0001
Control M 0.291 0.0157 190 0.260 0.322 36.913 <.0001
Permethrin_orange M 0.290 0.0157 190 0.259 0.321 36.819 <.0001
Confidence level used: 0.95
Intervals are back-transformed from the sqrt scale
Tests are performed on the sqrt scale
$contrasts
contrast estimate SE df lower.CL
Control F - Permethrin_orange F 0.000961 0.0120 190 -0.0300
Control F - Control M -0.018028 0.0121 190 -0.0493
Control F - Permethrin_orange M -0.017067 0.0166 190 -0.0601
Permethrin_orange F - Control M -0.018989 0.0174 190 -0.0640
Permethrin_orange F - Permethrin_orange M -0.018028 0.0121 190 -0.0493
Control M - Permethrin_orange M 0.000961 0.0120 190 -0.0300
upper.CL t.ratio p.value
0.0319 0.080 0.9998
0.0133 -1.492 0.4444
0.0260 -1.027 0.7339
0.0260 -1.093 0.6940
0.0133 -1.492 0.4444
0.0319 0.080 0.9998
Note: contrasts are still on the sqrt scale
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
P value adjustment: tukey method for comparing a family of 4 estimates ### Traveled distance Moving
ggplot(data_orange_inf20)+
geom_boxplot(aes(x=Traitement, y=Traveled_Dist_Moving, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Traveled_Dist_Moving, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
modTVM <- glmmTMB(Traveled_Dist_Moving~Traitement*sexe+(1|Replicat),
data=data_orange_inf20)
Anova(modTVM)Analysis of Deviance Table (Type II Wald chisquare tests)
Response: Traveled_Dist_Moving
Chisq Df Pr(>Chisq)
Traitement 0.7800 1 0.3771
sexe 1.1144 1 0.2911
Traitement:sexe 0.9158 1 0.3386modTVM <- glmmTMB(Traveled_Dist_Moving~Traitement+sexe+(1|Replicat),
data=data_orange_inf20)
res <- simulateResiduals(modTVM)
plot(res)
summary(modTVM) Family: gaussian ( identity )
Formula: Traveled_Dist_Moving ~ Traitement + sexe + (1 | Replicat)
Data: data_orange_inf20
AIC BIC logLik deviance df.resid
2817.6 2833.9 -1403.8 2807.6 190
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Replicat (Intercept) 13379 115.7
Residual 100028 316.3
Number of obs: 195, groups: Replicat, 5
Dispersion estimate for gaussian family (sigma^2): 1e+05
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 857.65 64.03 13.395 <2e-16 ***
TraitementPermethrin_orange -40.11 45.53 -0.881 0.378
sexeM 48.49 46.03 1.054 0.292
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(modTVM, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe emmean SE df lower.CL upper.CL t.ratio p.value
Control F 858 64.0 190 731 984 13.395 <.0001
Permethrin_orange F 818 65.5 190 688 947 12.482 <.0001
Control M 906 65.5 190 777 1035 13.832 <.0001
Permethrin_orange M 866 65.5 190 737 995 13.221 <.0001
Confidence level used: 0.95
$contrasts
contrast estimate SE df lower.CL upper.CL
Control F - Permethrin_orange F 40.11 45.5 190 -77.9 158.1
Control F - Control M -48.49 46.0 190 -167.8 70.8
Control F - Permethrin_orange M -8.38 63.2 190 -172.3 155.5
Permethrin_orange F - Control M -88.60 66.2 190 -260.2 83.0
Permethrin_orange F - Permethrin_orange M -48.49 46.0 190 -167.8 70.8
Control M - Permethrin_orange M 40.11 45.5 190 -77.9 158.1
t.ratio p.value
0.881 0.8147
-1.054 0.7181
-0.132 0.9992
-1.338 0.5397
-1.054 0.7181
0.881 0.8147
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
P value adjustment: tukey method for comparing a family of 4 estimates ### Average speed moving
ggplot(data_orange_inf20)+
geom_boxplot(aes(x=Traitement, y=Average_Speed_Moving, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Average_Speed_Moving, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
modAVM <- glmmTMB(Average_Speed_Moving~Traitement*sexe+(1|Replicat),
data=data_orange_inf20)
Anova(modAVM)Analysis of Deviance Table (Type II Wald chisquare tests)
Response: Average_Speed_Moving
Chisq Df Pr(>Chisq)
Traitement 2.5755 1 0.1085
sexe 0.5816 1 0.4457
Traitement:sexe 0.3221 1 0.5704modAVM <- glmmTMB(Average_Speed_Moving~Traitement+sexe+(1|Replicat),
data=data_orange_inf20)
res <- simulateResiduals(modAVM)
plot(res)
summary(modAVM) Family: gaussian ( identity )
Formula: Average_Speed_Moving ~ Traitement + sexe + (1 | Replicat)
Data: data_orange_inf20
AIC BIC logLik deviance df.resid
-290.3 -273.9 150.1 -300.3 190
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Replicat (Intercept) 0.001221 0.03494
Residual 0.012050 0.10977
Number of obs: 195, groups: Replicat, 5
Dispersion estimate for gaussian family (sigma^2): 0.0121
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.90172 0.02039 44.23 <2e-16 ***
TraitementPermethrin_orange -0.02534 0.01580 -1.60 0.109
sexeM -0.01216 0.01595 -0.76 0.446
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(modAVM, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe emmean SE df lower.CL upper.CL t.ratio p.value
Control F 0.902 0.0204 190 0.862 0.942 44.230 <.0001
Permethrin_orange F 0.876 0.0209 190 0.835 0.918 41.848 <.0001
Control M 0.890 0.0209 190 0.848 0.931 42.481 <.0001
Permethrin_orange M 0.864 0.0209 190 0.823 0.906 41.299 <.0001
Confidence level used: 0.95
$contrasts
contrast estimate SE df lower.CL
Control F - Permethrin_orange F 0.0253 0.0158 190 -0.0156
Control F - Control M 0.0122 0.0160 190 -0.0292
Control F - Permethrin_orange M 0.0375 0.0219 190 -0.0193
Permethrin_orange F - Control M -0.0132 0.0230 190 -0.0727
Permethrin_orange F - Permethrin_orange M 0.0122 0.0160 190 -0.0292
Control M - Permethrin_orange M 0.0253 0.0158 190 -0.0156
upper.CL t.ratio p.value
0.0663 1.604 0.3790
0.0535 0.762 0.8714
0.0943 1.710 0.3212
0.0464 -0.574 0.9398
0.0535 0.762 0.8714
0.0663 1.604 0.3790
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
P value adjustment: tukey method for comparing a family of 4 estimates ### Max bout inactiv
data_orange_inf20_sub <- data_orange_inf20 %>% dplyr::filter(Max_bout_inactiv<1500)
ggplot(data_orange_inf20)+
geom_boxplot(aes(x=Traitement, y=Max_bout_inactiv, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Max_bout_inactiv, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
ggplot(data_orange_inf20_sub)+
geom_boxplot(aes(x=Traitement, y=Max_bout_inactiv, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Max_bout_inactiv, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
hist(data_orange_inf20_sub$Max_bout_inactiv)
modMI <- glmmTMB(Max_bout_inactiv~Traitement*sexe+(1|Replicat),
data=data_orange_inf20_sub, family=nbinom2())
Anova(modMI)Analysis of Deviance Table (Type II Wald chisquare tests)
Response: Max_bout_inactiv
Chisq Df Pr(>Chisq)
Traitement 1.6419 1 0.2001
sexe 0.2349 1 0.6279
Traitement:sexe 0.7133 1 0.3984modMI <- glmmTMB(Max_bout_inactiv~Traitement+sexe+(1|Replicat),
data=data_orange_inf20_sub, family=nbinom2(), zi=~1)
summary(modMI) Family: nbinom2 ( log )
Formula: Max_bout_inactiv ~ Traitement + sexe + (1 | Replicat)
Zero inflation: ~1
Data: data_orange_inf20_sub
AIC BIC logLik deviance df.resid
2173.0 2192.5 -1080.5 2161.0 186
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Replicat (Intercept) 0.02673 0.1635
Number of obs: 192, groups: Replicat, 5
Dispersion parameter for nbinom2 family (): 2.19
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 4.77913 0.11128 42.95 <2e-16 ***
TraitementPermethrin_orange -0.12795 0.10006 -1.28 0.201
sexeM 0.04833 0.09950 0.49 0.627
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Zero-inflation model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -21.41 3214.48 -0.007 0.995res <- simulateResiduals(modMI)
plot(res)# 
emmeans(modMI, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe response SE df asymp.LCL asymp.UCL null z.ratio
Control F 119 13.2 Inf 95.7 148 1 42.948
Permethrin_orange F 105 11.8 Inf 84.0 131 1 41.263
Control M 125 14.1 Inf 100.1 156 1 42.711
Permethrin_orange M 110 12.7 Inf 87.6 138 1 40.665
p.value
<.0001
<.0001
<.0001
<.0001
Confidence level used: 0.95
Intervals are back-transformed from the log scale
Tests are performed on the log scale
$contrasts
contrast ratio SE df asymp.LCL asymp.UCL
Control F / Permethrin_orange F 1.137 0.1137 Inf 0.879 1.47
Control F / Control M 0.953 0.0948 Inf 0.738 1.23
Control F / Permethrin_orange M 1.083 0.1538 Inf 0.752 1.56
Permethrin_orange F / Control M 0.838 0.1175 Inf 0.585 1.20
Permethrin_orange F / Permethrin_orange M 0.953 0.0948 Inf 0.738 1.23
Control M / Permethrin_orange M 1.137 0.1137 Inf 0.879 1.47
null z.ratio p.value
1 1.279 0.5765
1 -0.486 0.9623
1 0.561 0.9437
1 -1.257 0.5902
1 -0.486 0.9623
1 1.279 0.5765
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
Intervals are back-transformed from the log scale
P value adjustment: tukey method for comparing a family of 4 estimates
Tests are performed on the log scale ### Max speed
ggplot(data_orange_inf20)+
geom_boxplot(aes(x=Traitement, y=Max_speed, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Max_speed, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
modMS <- glmmTMB(Max_speed~Traitement*sexe+(1|Replicat),
data=data_orange_inf20)
Anova(modMS)Analysis of Deviance Table (Type II Wald chisquare tests)
Response: Max_speed
Chisq Df Pr(>Chisq)
Traitement 1.0983 1 0.2946
sexe 0.1671 1 0.6827
Traitement:sexe 0.1907 1 0.6624modMS <- glmmTMB(Max_speed~Traitement+sexe+(1|Replicat),
data=data_orange_inf20)
summary(modMS) Family: gaussian ( identity )
Formula: Max_speed ~ Traitement + sexe + (1 | Replicat)
Data: data_orange_inf20
AIC BIC logLik deviance df.resid
10.8 27.1 -0.4 0.8 188
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Replicat (Intercept) 0.006635 0.08145
Residual 0.056243 0.23716
Number of obs: 193, groups: Replicat, 5
Dispersion estimate for gaussian family (sigma^2): 0.0562
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.01829 0.04626 65.25 <2e-16 ***
TraitementPermethrin_orange -0.03597 0.03434 -1.05 0.295
sexeM 0.01414 0.03461 0.41 0.683
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res <- simulateResiduals(modMS)
plot(res)
emmeans(modMS, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe emmean SE df lower.CL upper.CL t.ratio p.value
Control F 3.02 0.0463 188 2.93 3.11 65.246 <.0001
Permethrin_orange F 2.98 0.0473 188 2.89 3.08 63.026 <.0001
Control M 3.03 0.0473 188 2.94 3.13 64.136 <.0001
Permethrin_orange M 3.00 0.0474 188 2.90 3.09 63.185 <.0001
Confidence level used: 0.95
$contrasts
contrast estimate SE df lower.CL
Control F - Permethrin_orange F 0.0360 0.0343 188 -0.0531
Control F - Control M -0.0141 0.0346 188 -0.1038
Control F - Permethrin_orange M 0.0218 0.0479 188 -0.1023
Permethrin_orange F - Control M -0.0501 0.0496 188 -0.1787
Permethrin_orange F - Permethrin_orange M -0.0141 0.0346 188 -0.1038
Control M - Permethrin_orange M 0.0360 0.0343 188 -0.0531
upper.CL t.ratio p.value
0.1250 1.047 0.7218
0.0756 -0.409 0.9769
0.1459 0.456 0.9684
0.0785 -1.010 0.7439
0.0756 -0.409 0.9769
0.1250 1.047 0.7218
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
P value adjustment: tukey method for comparing a family of 4 estimates ggplot(data_orange_SP)+
geom_boxplot(aes(x=Traitement, y=Latency, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Latency, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
hist(data_orange_SP$Latency)
modLat <- glmmTMB(Latency~Traitement+sexe+(1|Replicat),
data=data_orange_SP, family=nbinom1())
res <- simulateResiduals(modLat)
plot(res)
summary(modLat) Family: nbinom1 ( log )
Formula: Latency ~ Traitement + sexe + (1 | Replicat)
Data: data_orange_SP
AIC BIC logLik deviance df.resid
835.8 852.2 -412.9 825.8 192
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Replicat (Intercept) 3.79e-09 6.157e-05
Number of obs: 197, groups: Replicat, 5
Dispersion parameter for nbinom1 family (): 58.1
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.6282 0.2647 6.152 7.64e-10 ***
TraitementPermethrin 0.4297 0.2200 1.953 0.0508 .
sexeM 0.3172 0.2185 1.452 0.1465
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(modLat, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe response SE df asymp.LCL asymp.UCL null z.ratio p.value
Control F 5.09 1.35 Inf 3.03 8.56 1 6.152 <.0001
Permethrin F 7.83 1.99 Inf 4.76 12.89 1 8.096 <.0001
Control M 7.00 1.81 Inf 4.21 11.61 1 7.523 <.0001
Permethrin M 10.75 2.53 Inf 6.78 17.04 1 10.109 <.0001
Confidence level used: 0.95
Intervals are back-transformed from the log scale
Tests are performed on the log scale
$contrasts
contrast ratio SE df asymp.LCL asymp.UCL null z.ratio
Control F / Permethrin F 0.651 0.143 Inf 0.370 1.15 1 -1.953
Control F / Control M 0.728 0.159 Inf 0.415 1.28 1 -1.452
Control F / Permethrin M 0.474 0.142 Inf 0.219 1.02 1 -2.491
Permethrin F / Control M 1.119 0.358 Inf 0.492 2.55 1 0.352
Permethrin F / Permethrin M 0.728 0.159 Inf 0.415 1.28 1 -1.452
Control M / Permethrin M 0.651 0.143 Inf 0.370 1.15 1 -1.953
p.value
0.2062
0.4669
0.0613
0.9851
0.4669
0.2062
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
Intervals are back-transformed from the log scale
P value adjustment: tukey method for comparing a family of 4 estimates
Tests are performed on the log scale ggplot(data_orange_SP)+
geom_boxplot(aes(x=Traitement, y=Prop_time_inside, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Prop_time_inside, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
modPTIW <- glmmTMB(sqrt(Prop_time_inside)~Traitement+sexe+(1|Replicat),
data=data_orange_SP)
res <- simulateResiduals(modPTIW)
plot(res)
summary(modPTIW) Family: gaussian ( identity )
Formula: sqrt(Prop_time_inside) ~ Traitement + sexe + (1 | Replicat)
Data: data_orange_SP
AIC BIC logLik deviance df.resid
-392.9 -376.5 201.5 -402.9 192
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Replicat (Intercept) 0.001720 0.04147
Residual 0.007135 0.08447
Number of obs: 197, groups: Replicat, 5
Dispersion estimate for gaussian family (sigma^2): 0.00713
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.788644 0.021103 37.37 <2e-16 ***
TraitementPermethrin -0.007736 0.012114 -0.64 0.523
sexeM -0.007968 0.012228 -0.65 0.515
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(modPTIW, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe response SE df lower.CL upper.CL t.ratio p.value
Control F 0.622 0.0333 192 0.558 0.689 37.372 <.0001
Permethrin F 0.610 0.0335 192 0.546 0.678 36.455 <.0001
Control M 0.609 0.0334 192 0.545 0.677 36.451 <.0001
Permethrin M 0.597 0.0330 192 0.534 0.664 36.210 <.0001
Confidence level used: 0.95
Intervals are back-transformed from the sqrt scale
Tests are performed on the sqrt scale
$contrasts
contrast estimate SE df lower.CL upper.CL t.ratio
Control F - Permethrin F 0.007736 0.0121 192 -0.0237 0.0391 0.639
Control F - Control M 0.007968 0.0122 192 -0.0237 0.0397 0.652
Control F - Permethrin M 0.015704 0.0167 192 -0.0276 0.0590 0.939
Permethrin F - Control M 0.000232 0.0177 192 -0.0456 0.0461 0.013
Permethrin F - Permethrin M 0.007968 0.0122 192 -0.0237 0.0397 0.652
Control M - Permethrin M 0.007736 0.0121 192 -0.0237 0.0391 0.639
p.value
0.9194
0.9149
0.7839
1.0000
0.9149
0.9194
Note: contrasts are still on the sqrt scale
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
P value adjustment: tukey method for comparing a family of 4 estimates ggplot(data_orange_SP)+
geom_boxplot(aes(x=Traitement, y=Prop_time_moving, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Prop_time_moving, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
modPTMs<- glmmTMB(sqrt(Prop_time_moving)~Traitement+sexe+(1|Replicat),
data=data_orange_SP)
res <- simulateResiduals(modPTMs)
plot(res)
summary(modPTMs) Family: gaussian ( identity )
Formula: sqrt(Prop_time_moving) ~ Traitement + sexe + (1 | Replicat)
Data: data_orange_SP
AIC BIC logLik deviance df.resid
-359.2 -342.7 184.6 -369.2 192
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Replicat (Intercept) 0.001648 0.04059
Residual 0.008511 0.09226
Number of obs: 197, groups: Replicat, 5
Dispersion estimate for gaussian family (sigma^2): 0.00851
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.45702 0.02122 21.534 <2e-16 ***
TraitementPermethrin -0.01710 0.01323 -1.292 0.196
sexeM 0.01383 0.01337 1.034 0.301
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(modPTMs, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe response SE df lower.CL upper.CL t.ratio p.value
Control F 0.209 0.0194 192 0.172 0.249 21.534 <.0001
Permethrin F 0.194 0.0190 192 0.158 0.233 20.367 <.0001
Control M 0.222 0.0203 192 0.183 0.264 21.795 <.0001
Permethrin M 0.206 0.0195 192 0.169 0.246 21.089 <.0001
Confidence level used: 0.95
Intervals are back-transformed from the sqrt scale
Tests are performed on the sqrt scale
$contrasts
contrast estimate SE df lower.CL upper.CL t.ratio
Control F - Permethrin F 0.01710 0.0132 192 -0.0172 0.0514 1.292
Control F - Control M -0.01383 0.0134 192 -0.0485 0.0208 -1.034
Control F - Permethrin M 0.00327 0.0183 192 -0.0441 0.0506 0.179
Permethrin F - Control M -0.03093 0.0193 192 -0.0810 0.0192 -1.600
Permethrin F - Permethrin M -0.01383 0.0134 192 -0.0485 0.0208 -1.034
Control M - Permethrin M 0.01710 0.0132 192 -0.0172 0.0514 1.292
p.value
0.5689
0.7298
0.9980
0.3813
0.7298
0.5689
Note: contrasts are still on the sqrt scale
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
P value adjustment: tukey method for comparing a family of 4 estimates ggplot(data_orange_SP)+
geom_boxplot(aes(x=Traitement, y=Nb_entries, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Nb_entries, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
modNbE<- glmmTMB(Nb_entries~Traitement+sexe+(1|Replicat),
data=data_orange_SP, family=nbinom1())
res <- simulateResiduals(modNbE)
plot(res)
summary(modNbE) Family: nbinom1 ( log )
Formula: Nb_entries ~ Traitement + sexe + (1 | Replicat)
Data: data_orange_SP
AIC BIC logLik deviance df.resid
2304.3 2320.8 -1147.2 2294.3 192
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Replicat (Intercept) 0.04675 0.2162
Number of obs: 197, groups: Replicat, 5
Dispersion parameter for nbinom1 family (): 25.9
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 5.44431 0.10448 52.11 <2e-16 ***
TraitementPermethrin 0.04563 0.04574 1.00 0.319
sexeM 0.06605 0.04564 1.45 0.148
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(modNbE, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe response SE df asymp.LCL asymp.UCL null z.ratio p.value
Control F 231 24.2 Inf 189 284 1 52.108 <.0001
Permethrin F 242 25.5 Inf 197 298 1 52.239 <.0001
Control M 247 26.0 Inf 201 304 1 52.461 <.0001
Permethrin M 259 27.0 Inf 211 318 1 53.183 <.0001
Confidence level used: 0.95
Intervals are back-transformed from the log scale
Tests are performed on the log scale
$contrasts
contrast ratio SE df asymp.LCL asymp.UCL null z.ratio
Control F / Permethrin F 0.955 0.0437 Inf 0.849 1.07 1 -0.998
Control F / Control M 0.936 0.0427 Inf 0.833 1.05 1 -1.447
Control F / Permethrin M 0.894 0.0561 Inf 0.761 1.05 1 -1.782
Permethrin F / Control M 0.980 0.0652 Inf 0.826 1.16 1 -0.307
Permethrin F / Permethrin M 0.936 0.0427 Inf 0.833 1.05 1 -1.447
Control M / Permethrin M 0.955 0.0437 Inf 0.849 1.07 1 -0.998
p.value
0.7508
0.4698
0.2820
0.9900
0.4698
0.7508
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
Intervals are back-transformed from the log scale
P value adjustment: tukey method for comparing a family of 4 estimates
Tests are performed on the log scale data_rouge_inf20 <- data_complete %>% dplyr::filter(Experiment=="Rouge"&Prop_time_lost<0.2)
data_rouge_SP <- data_complete_SP %>% dplyr::filter(Experiment=="Rouge"&Prop_time_lost<0.2)ggplot(data_rouge_inf20)+
geom_boxplot(aes(x=Traitement, y=Prop_time_moving, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Prop_time_moving, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
modPTM <- glmmTMB(sqrt(Prop_time_moving)~Traitement+sexe+(1|Replicat),
data=data_rouge_inf20)
res <- simulateResiduals(modPTM)
plot(res)
summary(modPTM) Family: gaussian ( identity )
Formula: sqrt(Prop_time_moving) ~ Traitement + sexe + (1 | Replicat)
Data: data_rouge_inf20
AIC BIC logLik deviance df.resid
-260.6 -244.0 135.3 -270.6 200
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Replicat (Intercept) 0.006165 0.07852
Residual 0.014773 0.12154
Number of obs: 205, groups: Replicat, 4
Dispersion estimate for gaussian family (sigma^2): 0.0148
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.29611 0.04189 7.069 1.56e-12 ***
TraitementPermethrin_rouge -0.06508 0.01760 -3.697 0.000218 ***
sexeM 0.01060 0.01702 0.623 0.533268
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(modPTM, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe response SE df lower.CL upper.CL t.ratio p.value
Control F 0.0877 0.0248 200 0.0456 0.1434 7.069 <.0001
Permethrin_rouge F 0.0534 0.0199 200 0.0213 0.0999 5.358 <.0001
Control M 0.0941 0.0258 200 0.0501 0.1518 7.296 <.0001
Permethrin_rouge M 0.0584 0.0208 200 0.0245 0.1067 5.604 <.0001
Confidence level used: 0.95
Intervals are back-transformed from the sqrt scale
Tests are performed on the sqrt scale
$contrasts
contrast estimate SE df lower.CL upper.CL
Control F - Permethrin_rouge F 0.0651 0.0176 200 0.01947 0.1107
Control F - Control M -0.0106 0.0170 200 -0.05469 0.0335
Control F - Permethrin_rouge M 0.0545 0.0242 200 -0.00826 0.1172
Permethrin_rouge F - Control M -0.0757 0.0247 200 -0.13980 -0.0116
Permethrin_rouge F - Permethrin_rouge M -0.0106 0.0170 200 -0.05469 0.0335
Control M - Permethrin_rouge M 0.0651 0.0176 200 0.01947 0.1107
t.ratio p.value
3.697 0.0016
-0.623 0.9246
2.250 0.1136
-3.058 0.0134
-0.623 0.9246
3.697 0.0016
Note: contrasts are still on the sqrt scale
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
P value adjustment: tukey method for comparing a family of 4 estimates ggplot(data_rouge_inf20)+
geom_boxplot(aes(x=Traitement, y=Traveled_Dist_Moving, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Traveled_Dist_Moving, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
hist(data_rouge_inf20$Traveled_Dist_Moving)
modTVM <- glmmTMB(Traveled_Dist_Moving~Traitement*sexe+(1|Replicat),
data=data_rouge_inf20)
Anova(modTVM)Analysis of Deviance Table (Type II Wald chisquare tests)
Response: Traveled_Dist_Moving
Chisq Df Pr(>Chisq)
Traitement 18.9752 1 1.324e-05 ***
sexe 1.7103 1 0.1910
Traitement:sexe 0.0024 1 0.9612
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1modTVM <- glmmTMB(Traveled_Dist_Moving~Traitement+sexe+(1|Replicat),
data=data_rouge_inf20, family=nbinom1())
summary(modTVM) Family: nbinom1 ( log )
Formula: Traveled_Dist_Moving ~ Traitement + sexe + (1 | Replicat)
Data: data_rouge_inf20
AIC BIC logLik deviance df.resid
2609.4 2626.0 -1299.7 2599.4 200
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Replicat (Intercept) 0.3074 0.5545
Number of obs: 205, groups: Replicat, 4
Dispersion parameter for nbinom1 family (): 188
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 5.51273 0.29205 18.876 < 2e-16 ***
TraitementPermethrin_rouge -0.51722 0.11285 -4.583 4.58e-06 ***
sexeM 0.03535 0.10208 0.346 0.729
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res <- simulateResiduals(modTVM)
plot(res)
# transfo
modTVM <- glmmTMB(sqrt(Traveled_Dist_Moving)~Traitement+sexe+(1|Replicat),
data=data_rouge_inf20)
res <- simulateResiduals(modTVM)
plot(res)
Anova(modTVM)# sors pas mais modèle meilleur avec variable sexe Analysis of Deviance Table (Type II Wald chisquare tests)
Response: sqrt(Traveled_Dist_Moving)
Chisq Df Pr(>Chisq)
Traitement 16.9192 1 3.901e-05 ***
sexe 0.6235 1 0.4298
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(modTVM, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe response SE df lower.CL upper.CL t.ratio p.value
Control F 221 67.5 200 107.9 374 6.546 <.0001
Permethrin_rouge F 124 51.9 200 42.7 247 4.776 <.0001
Control M 242 70.9 200 122.4 402 6.829 <.0001
Permethrin_rouge M 140 55.1 200 52.3 270 5.074 <.0001
Confidence level used: 0.95
Intervals are back-transformed from the sqrt scale
Tests are performed on the sqrt scale
$contrasts
contrast estimate SE df lower.CL upper.CL
Control F - Permethrin_rouge F 3.731 0.907 200 1.381 6.08
Control F - Control M -0.692 0.877 200 -2.964 1.58
Control F - Permethrin_rouge M 3.038 1.248 200 -0.194 6.27
Permethrin_rouge F - Control M -4.423 1.275 200 -7.726 -1.12
Permethrin_rouge F - Permethrin_rouge M -0.692 0.877 200 -2.964 1.58
Control M - Permethrin_rouge M 3.731 0.907 200 1.381 6.08
t.ratio p.value
4.113 0.0003
-0.790 0.8591
2.435 0.0738
-3.468 0.0036
-0.790 0.8591
4.113 0.0003
Note: contrasts are still on the sqrt scale
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
P value adjustment: tukey method for comparing a family of 4 estimates ggplot(data_rouge_inf20)+
geom_boxplot(aes(x=Traitement, y=Average_Speed_Moving, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Average_Speed_Moving, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
modAVM <- glmmTMB(Average_Speed_Moving~Traitement*sexe+(1|Replicat),
data=data_orange_inf20)
Anova(modAVM)Analysis of Deviance Table (Type II Wald chisquare tests)
Response: Average_Speed_Moving
Chisq Df Pr(>Chisq)
Traitement 2.5755 1 0.1085
sexe 0.5816 1 0.4457
Traitement:sexe 0.3221 1 0.5704modAVM <- glmmTMB(Average_Speed_Moving~Traitement+sexe+(1|Replicat),
data=data_rouge_inf20)
res <- simulateResiduals(modAVM)
plot(res)
summary(modAVM) Family: gaussian ( identity )
Formula: Average_Speed_Moving ~ Traitement + sexe + (1 | Replicat)
Data: data_rouge_inf20
AIC BIC logLik deviance df.resid
-145.0 -128.6 77.5 -155.0 193
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Replicat (Intercept) 0.002212 0.04704
Residual 0.025954 0.16110
Number of obs: 198, groups: Replicat, 4
Dispersion estimate for gaussian family (sigma^2): 0.026
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.73279 0.03054 23.991 <2e-16 ***
TraitementPermethrin_rouge -0.03413 0.02356 -1.449 0.147
sexeM -0.01548 0.02294 -0.675 0.500
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(modAVM, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe emmean SE df lower.CL upper.CL t.ratio p.value
Control F 0.733 0.0305 193 0.673 0.793 23.991 <.0001
Permethrin_rouge F 0.699 0.0330 193 0.634 0.764 21.158 <.0001
Control M 0.717 0.0309 193 0.656 0.778 23.195 <.0001
Permethrin_rouge M 0.683 0.0329 193 0.618 0.748 20.741 <.0001
Confidence level used: 0.95
$contrasts
contrast estimate SE df lower.CL upper.CL
Control F - Permethrin_rouge F 0.0341 0.0236 193 -0.0269 0.0952
Control F - Control M 0.0155 0.0229 193 -0.0440 0.0749
Control F - Permethrin_rouge M 0.0496 0.0324 193 -0.0345 0.1337
Permethrin_rouge F - Control M -0.0186 0.0333 193 -0.1050 0.0677
Permethrin_rouge F - Permethrin_rouge M 0.0155 0.0229 193 -0.0440 0.0749
Control M - Permethrin_rouge M 0.0341 0.0236 193 -0.0269 0.0952
t.ratio p.value
1.449 0.4705
0.675 0.9065
1.529 0.4219
-0.560 0.9438
0.675 0.9065
1.449 0.4705
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
P value adjustment: tukey method for comparing a family of 4 estimates ggplot(data_rouge_inf20)+
geom_boxplot(aes(x=Traitement, y=Max_bout_inactiv, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Max_bout_inactiv, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
modMI <- glmmTMB(Max_bout_inactiv~Traitement+sexe+(1|Replicat),
data=data_rouge_inf20, family=nbinom2(), zi=~1)
res <- simulateResiduals(modMI)
plot(res)
summary(modMI) Family: nbinom2 ( log )
Formula: Max_bout_inactiv ~ Traitement + sexe + (1 | Replicat)
Zero inflation: ~1
Data: data_rouge_inf20
AIC BIC logLik deviance df.resid
3037.4 3057.3 -1512.7 3025.4 196
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Replicat (Intercept) 0.1283 0.3582
Number of obs: 202, groups: Replicat, 4
Dispersion parameter for nbinom2 family (): 1.22
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 6.4222 0.2111 30.429 <2e-16 ***
TraitementPermethrin_rouge 0.1765 0.1357 1.301 0.193
sexeM 0.1704 0.1285 1.326 0.185
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Zero-inflation model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -20.73 2230.96 -0.009 0.993emmeans(modMI, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe response SE df asymp.LCL asymp.UCL null z.ratio
Control F 615 130 Inf 407 931 1 30.429
Permethrin_rouge F 734 162 Inf 476 1132 1 29.834
Control M 730 154 Inf 482 1104 1 31.174
Permethrin_rouge M 870 196 Inf 559 1355 1 29.995
p.value
<.0001
<.0001
<.0001
<.0001
Confidence level used: 0.95
Intervals are back-transformed from the log scale
Tests are performed on the log scale
$contrasts
contrast ratio SE df asymp.LCL asymp.UCL
Control F / Permethrin_rouge F 0.838 0.114 Inf 0.592 1.19
Control F / Control M 0.843 0.108 Inf 0.606 1.17
Control F / Permethrin_rouge M 0.707 0.135 Inf 0.432 1.16
Permethrin_rouge F / Control M 1.006 0.183 Inf 0.631 1.61
Permethrin_rouge F / Permethrin_rouge M 0.843 0.108 Inf 0.606 1.17
Control M / Permethrin_rouge M 0.838 0.114 Inf 0.592 1.19
null z.ratio p.value
1 -1.301 0.5622
1 -1.326 0.5461
1 -1.810 0.2686
1 0.034 1.0000
1 -1.326 0.5461
1 -1.301 0.5622
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
Intervals are back-transformed from the log scale
P value adjustment: tukey method for comparing a family of 4 estimates
Tests are performed on the log scale ggplot(data_rouge_inf20)+
geom_boxplot(aes(x=Traitement, y=Max_bout_activ, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Max_bout_activ, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
# modMAct <- glmmTMB(Max_bout_activ~Traitement+sexe+(1|Replicat),
# data=data_rouge_inf20, family=nbinom2(), zi=~1)
#
#
# res <- simulateResiduals(modMI)
# plot(res)
#
# summary(modMI)
# emmeans(modMI, pairwise~Traitement+sexe, infer=TRUE, type="response")ggplot(data_rouge_inf20)+
geom_boxplot(aes(x=Traitement, y=Max_speed, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Max_speed, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
modMS <- glmmTMB(Max_speed~Traitement+sexe+(1|Replicat),
data=data_rouge_inf20)
summary(modMS) Family: gaussian ( identity )
Formula: Max_speed ~ Traitement + sexe + (1 | Replicat)
Data: data_rouge_inf20
AIC BIC logLik deviance df.resid
387.1 403.6 -188.5 377.1 197
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Replicat (Intercept) 0.1560 0.3950
Residual 0.3572 0.5977
Number of obs: 202, groups: Replicat, 4
Dispersion estimate for gaussian family (sigma^2): 0.357
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.4800 0.2103 11.793 <2e-16 ***
TraitementPermethrin_rouge -0.1026 0.0871 -1.178 0.2389
sexeM -0.1520 0.0843 -1.803 0.0713 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1res <- simulateResiduals(modMS)
plot(res)
emmeans(modMS, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe emmean SE df lower.CL upper.CL t.ratio p.value
Control F 2.48 0.210 197 2.07 2.89 11.793 <.0001
Permethrin_rouge F 2.38 0.216 197 1.95 2.80 11.000 <.0001
Control M 2.33 0.211 197 1.91 2.74 11.025 <.0001
Permethrin_rouge M 2.23 0.216 197 1.80 2.65 10.300 <.0001
Confidence level used: 0.95
$contrasts
contrast estimate SE df lower.CL upper.CL
Control F - Permethrin_rouge F 0.1026 0.0871 197 -0.1231 0.328
Control F - Control M 0.1520 0.0843 197 -0.0664 0.370
Control F - Permethrin_rouge M 0.2546 0.1196 197 -0.0552 0.564
Permethrin_rouge F - Control M 0.0494 0.1228 197 -0.2689 0.368
Permethrin_rouge F - Permethrin_rouge M 0.1520 0.0843 197 -0.0664 0.370
Control M - Permethrin_rouge M 0.1026 0.0871 197 -0.1231 0.328
t.ratio p.value
1.178 0.6416
1.803 0.2749
2.129 0.1474
0.402 0.9779
1.803 0.2749
1.178 0.6416
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
P value adjustment: tukey method for comparing a family of 4 estimates ggplot(data_rouge_SP)+
geom_boxplot(aes(x=Traitement, y=Latency, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Latency, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
hist(data_orange_SP$Latency)
modLat <- glmmTMB(Latency~Traitement+sexe+(1|Replicat),
data=data_rouge_SP, family=nbinom1())
res <- simulateResiduals(modLat)
plot(res)
summary(modLat) Family: nbinom1 ( log )
Formula: Latency ~ Traitement + sexe + (1 | Replicat)
Data: data_rouge_SP
AIC BIC logLik deviance df.resid
970.8 987.5 -480.4 960.8 203
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Replicat (Intercept) 3.366e-09 5.801e-05
Number of obs: 208, groups: Replicat, 4
Dispersion parameter for nbinom1 family (): 470
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.5949 0.2916 12.33 <2e-16 ***
TraitementPermethrin_rouge -0.3291 0.2402 -1.37 0.171
sexeM 0.1179 0.2266 0.52 0.603
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(modLat, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe response SE df asymp.LCL asymp.UCL null z.ratio
Control F 36.4 10.62 Inf 20.6 64.5 1 12.327
Permethrin_rouge F 26.2 8.71 Inf 13.7 50.3 1 9.824
Control M 41.0 11.81 Inf 23.3 72.1 1 12.874
Permethrin_rouge M 29.5 9.37 Inf 15.8 55.0 1 10.648
p.value
<.0001
<.0001
<.0001
<.0001
Confidence level used: 0.95
Intervals are back-transformed from the log scale
Tests are performed on the log scale
$contrasts
contrast ratio SE df asymp.LCL asymp.UCL
Control F / Permethrin_rouge F 1.390 0.334 Inf 0.750 2.58
Control F / Control M 0.889 0.201 Inf 0.497 1.59
Control F / Permethrin_rouge M 1.235 0.393 Inf 0.545 2.80
Permethrin_rouge F / Control M 0.639 0.218 Inf 0.266 1.54
Permethrin_rouge F / Permethrin_rouge M 0.889 0.201 Inf 0.497 1.59
Control M / Permethrin_rouge M 1.390 0.334 Inf 0.750 2.58
null z.ratio p.value
1 1.370 0.5181
1 -0.520 0.9542
1 0.663 0.9109
1 -1.309 0.5573
1 -0.520 0.9542
1 1.370 0.5181
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
Intervals are back-transformed from the log scale
P value adjustment: tukey method for comparing a family of 4 estimates
Tests are performed on the log scale ggplot(data_rouge_SP)+
geom_boxplot(aes(x=Traitement, y=Prop_time_inside, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Prop_time_inside, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
modPTIW <- glmmTMB(sqrt(Prop_time_inside)~Traitement+sexe+(1|Replicat),
data=data_rouge_SP)
res <- simulateResiduals(modPTIW)
plot(res)
summary(modPTIW) Family: gaussian ( identity )
Formula: sqrt(Prop_time_inside) ~ Traitement + sexe + (1 | Replicat)
Data: data_rouge_SP
AIC BIC logLik deviance df.resid
-242.1 -225.4 126.0 -252.1 203
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Replicat (Intercept) 0.003559 0.05966
Residual 0.016665 0.12909
Number of obs: 208, groups: Replicat, 4
Dispersion estimate for gaussian family (sigma^2): 0.0167
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.81408 0.03358 24.242 < 2e-16 ***
TraitementPermethrin_rouge 0.05131 0.01856 2.764 0.00571 **
sexeM -0.02488 0.01794 -1.387 0.16550
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(modPTIW, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe response SE df lower.CL upper.CL t.ratio p.value
Control F 0.663 0.0547 203 0.559 0.775 24.242 <.0001
Permethrin_rouge F 0.749 0.0610 203 0.633 0.874 24.552 <.0001
Control M 0.623 0.0532 203 0.522 0.732 23.415 <.0001
Permethrin_rouge M 0.706 0.0591 203 0.595 0.828 23.893 <.0001
Confidence level used: 0.95
Intervals are back-transformed from the sqrt scale
Tests are performed on the sqrt scale
$contrasts
contrast estimate SE df lower.CL upper.CL
Control F - Permethrin_rouge F -0.0513 0.0186 203 -0.09940 -0.00323
Control F - Control M 0.0249 0.0179 203 -0.02160 0.07137
Control F - Permethrin_rouge M -0.0264 0.0256 203 -0.09265 0.03979
Permethrin_rouge F - Control M 0.0762 0.0261 203 0.00866 0.14373
Permethrin_rouge F - Permethrin_rouge M 0.0249 0.0179 203 -0.02160 0.07137
Control M - Permethrin_rouge M -0.0513 0.0186 203 -0.09940 -0.00323
t.ratio p.value
-2.764 0.0314
1.387 0.5091
-1.034 0.7298
2.923 0.0200
1.387 0.5091
-2.764 0.0314
Note: contrasts are still on the sqrt scale
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
P value adjustment: tukey method for comparing a family of 4 estimates ggplot(data_rouge_SP)+
geom_boxplot(aes(x=Traitement, y=Prop_time_moving, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Prop_time_moving, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
modPTMs<- glmmTMB(sqrt(Prop_time_moving)~Traitement+sexe+(1|Replicat),
data=data_rouge_SP)
res <- simulateResiduals(modPTMs)
plot(res)
summary(modPTMs) Family: gaussian ( identity )
Formula: sqrt(Prop_time_moving) ~ Traitement + sexe + (1 | Replicat)
Data: data_rouge_SP
AIC BIC logLik deviance df.resid
-314.1 -297.4 162.1 -324.1 203
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Replicat (Intercept) 0.004425 0.06652
Residual 0.011670 0.10803
Number of obs: 208, groups: Replicat, 4
Dispersion estimate for gaussian family (sigma^2): 0.0117
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.220887 0.035702 6.187 6.13e-10 ***
TraitementPermethrin_rouge -0.046528 0.015531 -2.996 0.00274 **
sexeM 0.008154 0.015020 0.543 0.58721
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(modPTMs, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe response SE df lower.CL upper.CL t.ratio p.value
Control F 0.0488 0.0158 203 0.0226 0.0848 6.187 <.0001
Permethrin_rouge F 0.0304 0.0128 203 0.0104 0.0610 4.736 <.0001
Control M 0.0525 0.0164 203 0.0251 0.0898 6.398 <.0001
Permethrin_rouge M 0.0333 0.0134 203 0.0121 0.0651 4.961 <.0001
Confidence level used: 0.95
Intervals are back-transformed from the sqrt scale
Tests are performed on the sqrt scale
$contrasts
contrast estimate SE df lower.CL upper.CL
Control F - Permethrin_rouge F 0.04653 0.0155 203 0.0063 0.08676
Control F - Control M -0.00815 0.0150 203 -0.0471 0.03076
Control F - Permethrin_rouge M 0.03837 0.0214 203 -0.0171 0.09381
Permethrin_rouge F - Control M -0.05468 0.0218 203 -0.1112 0.00181
Permethrin_rouge F - Permethrin_rouge M -0.00815 0.0150 203 -0.0471 0.03076
Control M - Permethrin_rouge M 0.04653 0.0155 203 0.0063 0.08676
t.ratio p.value
2.996 0.0161
-0.543 0.9484
1.793 0.2796
-2.507 0.0618
-0.543 0.9484
2.996 0.0161
Note: contrasts are still on the sqrt scale
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
P value adjustment: tukey method for comparing a family of 4 estimates ggplot(data_rouge_SP)+
geom_boxplot(aes(x=Traitement, y=Nb_entries, colour=Traitement))+
geom_jitter(aes(x=Traitement, y=Nb_entries, colour=Traitement))+
theme(axis.text.x = element_blank(), axis.ticks = element_blank())
modNbE<- glmmTMB(Nb_entries~Traitement+sexe+(1|Replicat),
data=data_rouge_SP, family=nbinom1())
res <- simulateResiduals(modNbE)
plot(res)
summary(modNbE) Family: nbinom1 ( log )
Formula: Nb_entries ~ Traitement + sexe + (1 | Replicat)
Data: data_rouge_SP
AIC BIC logLik deviance df.resid
2270.6 2287.3 -1130.3 2260.6 203
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Replicat (Intercept) 0.09915 0.3149
Number of obs: 208, groups: Replicat, 4
Dispersion parameter for nbinom1 family (): 41
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 4.66766 0.17287 27.000 < 2e-16 ***
TraitementPermethrin_rouge -0.40131 0.08732 -4.596 4.31e-06 ***
sexeM 0.02320 0.08136 0.285 0.776
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans(modNbE, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe response SE df asymp.LCL asymp.UCL null z.ratio
Control F 106.4 18.4 Inf 75.9 149 1 27.000
Permethrin_rouge F 71.3 13.0 Inf 49.8 102 1 23.367
Control M 108.9 18.9 Inf 77.6 153 1 27.073
Permethrin_rouge M 72.9 13.4 Inf 50.9 105 1 23.350
p.value
<.0001
<.0001
<.0001
<.0001
Confidence level used: 0.95
Intervals are back-transformed from the log scale
Tests are performed on the log scale
$contrasts
contrast ratio SE df asymp.LCL asymp.UCL
Control F / Permethrin_rouge F 1.494 0.1304 Inf 1.194 1.869
Control F / Control M 0.977 0.0795 Inf 0.793 1.204
Control F / Permethrin_rouge M 1.460 0.1759 Inf 1.071 1.989
Permethrin_rouge F / Control M 0.654 0.0773 Inf 0.483 0.886
Permethrin_rouge F / Permethrin_rouge M 0.977 0.0795 Inf 0.793 1.204
Control M / Permethrin_rouge M 1.494 0.1304 Inf 1.194 1.869
null z.ratio p.value
1 4.596 <.0001
1 -0.285 0.9919
1 3.138 0.0092
1 -3.592 0.0019
1 -0.285 0.9919
1 4.596 <.0001
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
Intervals are back-transformed from the log scale
P value adjustment: tukey method for comparing a family of 4 estimates
Tests are performed on the log scale library(survminer)
library(survival)
data_complete <- data_complete %>% mutate(surv_bin = ifelse(!is.na(date_mort2), 1, NA))
surv_time <- survfit(Surv(duree_vie,surv_bin)~Traitement, data=data_complete) # survival object for graph
ggsurvplot(surv_time, data = data_complete, facet.by = "sexe", legend.title = "Pupaison time")#KM plot
surv_time_s <- survfit(Surv(duree_vie,surv_bin)~sexe, data=data_complete) # survival object for graph
ggsurvplot(surv_time_s, data = data_complete, facet.by = "Traitement", legend.title = "Pupaison time")#KM plot
summary(surv_time)# 100 obs lost due to missingness. NA= non observed, may I infer on the day before ? Call: survfit(formula = Surv(duree_vie, surv_bin) ~ Traitement, data = data_complete)
120 observations deleted due to missingness
Traitement=Control
time n.risk n.event survival std.err lower 95% CI upper 95% CI
-5 207 1 0.99517 0.00482 0.985768 1.0000
8 206 1 0.99034 0.00680 0.977102 1.0000
9 205 5 0.96618 0.01256 0.941871 0.9911
10 200 10 0.91787 0.01908 0.881224 0.9560
11 190 9 0.87440 0.02303 0.830396 0.9207
12 181 2 0.86473 0.02377 0.819377 0.9126
13 179 11 0.81159 0.02718 0.760035 0.8667
14 168 1 0.80676 0.02744 0.754730 0.8624
15 167 2 0.79710 0.02795 0.744157 0.8538
17 165 2 0.78744 0.02844 0.733633 0.8452
18 163 2 0.77778 0.02890 0.723156 0.8365
19 161 4 0.75845 0.02975 0.702331 0.8191
20 157 10 0.71014 0.03153 0.650953 0.7747
21 147 1 0.70531 0.03169 0.645864 0.7702
22 146 15 0.63285 0.03350 0.570477 0.7020
24 131 13 0.57005 0.03441 0.506443 0.6416
25 118 18 0.48309 0.03473 0.419596 0.5562
26 100 16 0.40580 0.03413 0.344126 0.4785
27 84 12 0.34783 0.03310 0.288636 0.4192
28 72 17 0.26570 0.03070 0.211855 0.3332
29 55 11 0.21256 0.02844 0.163535 0.2763
30 44 3 0.19807 0.02770 0.150581 0.2605
31 41 1 0.19324 0.02744 0.146286 0.2553
32 40 17 0.11111 0.02184 0.075582 0.1633
33 23 1 0.10628 0.02142 0.071596 0.1578
34 22 5 0.08213 0.01908 0.052082 0.1295
35 17 2 0.07246 0.01802 0.044510 0.1180
36 15 3 0.05797 0.01624 0.033475 0.1004
37 12 1 0.05314 0.01559 0.029901 0.0944
38 11 4 0.03382 0.01256 0.016326 0.0700
39 7 2 0.02415 0.01067 0.010161 0.0574
42 5 2 0.01449 0.00831 0.004713 0.0446
45 3 1 0.00966 0.00680 0.002433 0.0384
46 2 1 0.00483 0.00482 0.000684 0.0341
51 1 1 0.00000 NaN NA NA
Traitement=Permethrin_orange
time n.risk n.event survival std.err lower 95% CI upper 95% CI
9 98 1 0.9898 0.0102 0.97010 1.0000
11 97 2 0.9694 0.0174 0.93587 1.0000
12 95 2 0.9490 0.0222 0.90640 0.9936
13 93 8 0.8673 0.0343 0.80272 0.9372
15 85 2 0.8469 0.0364 0.77857 0.9213
21 83 3 0.8163 0.0391 0.74315 0.8967
22 80 7 0.7449 0.0440 0.66340 0.8364
24 73 9 0.6531 0.0481 0.56530 0.7544
25 64 8 0.5714 0.0500 0.48139 0.6783
27 56 9 0.4796 0.0505 0.39021 0.5894
28 47 5 0.4286 0.0500 0.34099 0.5387
29 42 9 0.3367 0.0477 0.25504 0.4446
30 33 1 0.3265 0.0474 0.24572 0.4339
31 32 2 0.3061 0.0466 0.22722 0.4124
32 30 6 0.2449 0.0434 0.17298 0.3467
34 24 7 0.1735 0.0382 0.11260 0.2672
36 17 5 0.1224 0.0331 0.07207 0.2080
37 12 4 0.0816 0.0277 0.04202 0.1586
38 8 4 0.0408 0.0200 0.01563 0.1066
41 4 1 0.0306 0.0174 0.01005 0.0933
43 3 1 0.0204 0.0143 0.00518 0.0804
44 2 1 0.0102 0.0102 0.00145 0.0717
51 1 1 0.0000 NaN NA NA
Traitement=Permethrin_rouge
time n.risk n.event survival std.err lower 95% CI upper 95% CI
-3 102 1 0.9902 0.00976 0.97126 1.0000
8 101 2 0.9706 0.01673 0.93835 1.0000
9 99 1 0.9608 0.01922 0.92384 0.9992
10 98 7 0.8922 0.03071 0.83395 0.9544
11 91 10 0.7941 0.04004 0.71940 0.8766
12 81 4 0.7549 0.04259 0.67588 0.8432
13 77 5 0.7059 0.04512 0.62277 0.8001
16 72 2 0.6863 0.04594 0.60188 0.7825
17 70 4 0.6471 0.04732 0.56066 0.7468
18 66 1 0.6373 0.04761 0.55046 0.7377
19 65 2 0.6176 0.04812 0.53019 0.7195
20 63 3 0.5882 0.04873 0.50008 0.6919
22 60 8 0.5098 0.04950 0.42146 0.6167
23 52 2 0.4902 0.04950 0.40218 0.5975
24 50 1 0.4804 0.04947 0.39259 0.5878
25 49 15 0.3333 0.04668 0.25333 0.4386
26 34 13 0.2059 0.04004 0.14064 0.3014
28 21 5 0.1569 0.03601 0.10003 0.2460
29 16 5 0.1078 0.03071 0.06171 0.1885
30 11 2 0.0882 0.02808 0.04728 0.1647
32 9 6 0.0294 0.01673 0.00965 0.0897
35 3 1 0.0196 0.01373 0.00497 0.0773
36 2 1 0.0098 0.00976 0.00139 0.0689
46 1 1 0.0000 NaN NA NAmedian(surv_time) 50
Traitement=Control 25
Traitement=Permethrin_orange 27
Traitement=Permethrin_rouge 23test_logrank <- survdiff(Surv(duree_vie,surv_bin) ~ Traitement, data = data_complete)
test_logrankCall:
survdiff(formula = Surv(duree_vie, surv_bin) ~ Traitement, data = data_complete)
n=407, 120 observations deleted due to missingness.
N Observed Expected (O-E)^2/E (O-E)^2/V
Traitement=Control 207 207 206.7 3.67e-04 8.68e-04
Traitement=Permethrin_orange 98 98 129.1 7.50e+00 1.31e+01
Traitement=Permethrin_rouge 102 102 71.2 1.34e+01 1.88e+01
Chisq= 24.7 on 2 degrees of freedom, p= 4e-06 test_logrank_s <- survdiff(Surv(duree_vie,surv_bin) ~ sexe, data = data_complete)
test_logrank_sCall:
survdiff(formula = Surv(duree_vie, surv_bin) ~ sexe, data = data_complete)
n=354, 173 observations deleted due to missingness.
N Observed Expected (O-E)^2/E (O-E)^2/V
sexe=F 174 174 188 1.02 2.61
sexe=M 180 180 166 1.16 2.61
Chisq= 2.6 on 1 degrees of freedom, p= 0.1 library(coxme)
# orange
data_orange <- data_complete %>% filter(Experiment=="Orange") %>% droplevels()
cox_orange <- coxme(Surv(duree_vie,surv_bin) ~ Traitement+sexe+(1|Replicat), data = data_orange)
summary(cox_orange)Mixed effects coxme model
Formula: Surv(duree_vie, surv_bin) ~ Traitement + sexe + (1 | Replicat)
Data: data_orange
events, n = 166, 166 (74 observations deleted due to missingness)
Random effects:
group variable sd variance
1 Replicat Intercept 0.009331707 8.708076e-05
Chisq df p AIC BIC
Integrated loglik 0.16 3.00 0.9836 -5.84 -15.18
Penalized loglik 0.18 2.01 0.9145 -3.84 -10.10
Fixed effects:
coef exp(coef) se(coef) z p
TraitementPermethrin_orange 0.05689 1.05854 0.15980 0.36 0.722
sexeM 0.02138 1.02161 0.15918 0.13 0.893# rouge
data_rouge <- data_complete %>% filter(Experiment=="Rouge") %>% droplevels()
cox_rouge <- coxme(Surv(duree_vie,surv_bin) ~ Traitement*sexe+(1|Replicat), data = data_rouge)
summary(cox_rouge)Mixed effects coxme model
Formula: Surv(duree_vie, surv_bin) ~ Traitement * sexe + (1 | Replicat)
Data: data_rouge
events, n = 188, 188 (99 observations deleted due to missingness)
Random effects:
group variable sd variance
1 Replicat Intercept 1.011207 1.022539
Chisq df p AIC BIC
Integrated loglik 70.38 4.00 1.887e-14 62.38 49.44
Penalized loglik 84.05 5.91 4.441e-16 72.24 53.13
Fixed effects:
coef exp(coef) se(coef) z p
TraitementPermethrin_rouge -0.01081 0.98925 0.21904 -0.05 0.9606
sexeM 0.48908 1.63081 0.20071 2.44 0.0148
TraitementPermethrin_rouge:sexeM 0.04699 1.04811 0.29977 0.16 0.8754emmeans(cox_rouge, pairwise~Traitement+sexe, infer=TRUE, type="response")$emmeans
Traitement sexe response SE df asymp.LCL asymp.UCL null z.ratio
Control F 0.763 0.100 Inf 0.589 0.987 1 -2.058
Permethrin_rouge F 0.754 0.108 Inf 0.570 0.999 1 -1.968
Control M 1.244 0.136 Inf 1.003 1.541 1 1.990
Permethrin_rouge M 1.289 0.182 Inf 0.978 1.700 1 1.803
p.value
0.0396
0.0491
0.0466
0.0714
Confidence level used: 0.95
Intervals are back-transformed from the log scale
Tests are performed on the log scale
$contrasts
contrast ratio SE df asymp.LCL asymp.UCL
Control F / Permethrin_rouge F 1.011 0.221 Inf 0.576 1.77
Control F / Control M 0.613 0.123 Inf 0.366 1.03
Control F / Permethrin_rouge M 0.591 0.131 Inf 0.335 1.04
Permethrin_rouge F / Control M 0.607 0.128 Inf 0.353 1.04
Permethrin_rouge F / Permethrin_rouge M 0.585 0.134 Inf 0.325 1.05
Control M / Permethrin_rouge M 0.964 0.199 Inf 0.567 1.64
null z.ratio p.value
1 0.049 1.0000
1 -2.437 0.0703
1 -2.374 0.0821
1 -2.368 0.0833
1 -2.342 0.0888
1 -0.175 0.9981
Confidence level used: 0.95
Conf-level adjustment: tukey method for comparing a family of 4 estimates
Intervals are back-transformed from the log scale
P value adjustment: tukey method for comparing a family of 4 estimates
Tests are performed on the log scale data_complete <- data_complete %>% mutate(nymph_bin = ifelse(!is.na(date_adulte2), 1, NA))
surv_time <- survfit(Surv(duree_vie,surv_bin)~Traitement, data=data_complete) # survival object for graph
ggsurvplot(surv_time, data = data_complete, facet.by = "sexe", legend.title = "Pupaison time")#KM plot
data_complete <- data_complete %>%
mutate(dvpt_ad=case_when(
is.na(date_adulte2) & !is.na(date_mort2) ~ 0,
!is.na(date_adulte2) ~ 1,
TRUE~ NA),
duree_ad= date_adulte2 - date_eclosion2)
data_orange <- data_orange %>%
mutate(dvpt_ad=case_when(
is.na(date_adulte2) & !is.na(date_mort2) ~ 0,
!is.na(date_adulte2) ~ 1,
TRUE~ NA),
duree_ad= date_adulte2 - date_eclosion2)
data_rouge <- data_rouge %>%
mutate(dvpt_ad=case_when(
is.na(date_adulte2) & !is.na(date_mort2) ~ 0,
!is.na(date_adulte2) ~ 1,
TRUE~ NA),
duree_ad= date_adulte2 - date_eclosion2)
surv_time <- survfit(Surv(duree_ad,dvpt_ad)~Traitement, data=data_complete) # survival object for graph
ggsurvplot(surv_time, data = data_complete, facet.by = "sexe", legend.title = "Pupaison time")#KM plot
median(surv_time) 50
Traitement=Control 11
Traitement=Permethrin_orange 13
Traitement=Permethrin_rouge 10test_logrank_ad <- survdiff(Surv(duree_ad,dvpt_ad) ~ Traitement, data = data_complete)
test_logrank_adCall:
survdiff(formula = Surv(duree_ad, dvpt_ad) ~ Traitement, data = data_complete)
n=460, 67 observations deleted due to missingness.
N Observed Expected (O-E)^2/E (O-E)^2/V
Traitement=Control 244 244 219.3 2.78 10.4
Traitement=Permethrin_orange 103 103 156.4 18.21 60.3
Traitement=Permethrin_rouge 113 113 84.3 9.76 21.9
Chisq= 65.9 on 2 degrees of freedom, p= 5e-15 # only pour les rouge
cox_ad_rouge <- coxme(Surv(duree_ad,dvpt_ad) ~ Traitement+(1|Replicat), data = data_rouge)
summary(cox_ad_rouge)Mixed effects coxme model
Formula: Surv(duree_ad, dvpt_ad) ~ Traitement + (1 | Replicat)
Data: data_rouge
events, n = 250, 250 (37 observations deleted due to missingness)
Random effects:
group variable sd variance
1 Replicat Intercept 1.298839 1.686982
Chisq df p AIC BIC
Integrated loglik 176.9 2.00 0 172.9 165.8
Penalized loglik 192.2 3.94 0 184.4 170.5
Fixed effects:
coef exp(coef) se(coef) z p
TraitementPermethrin_rouge -0.6764 0.5085 0.1357 -4.99 6.17e-07# only pour les orang
cox_ad_orange <- coxme(Surv(duree_ad,dvpt_ad) ~ Traitement+(1|Replicat), data = data_orange)
summary(cox_ad_orange)Mixed effects coxme model
Formula: Surv(duree_ad, dvpt_ad) ~ Traitement + (1 | Replicat)
Data: data_orange
events, n = 210, 210 (30 observations deleted due to missingness)
Random effects:
group variable sd variance
1 Replicat Intercept 0.8386643 0.7033579
Chisq df p AIC BIC
Integrated loglik 81.60 2.00 0 77.60 70.91
Penalized loglik 98.69 4.85 0 88.99 72.76
Fixed effects:
coef exp(coef) se(coef) z p
TraitementPermethrin_orange -0.3324 0.7172 0.1410 -2.36 0.0184# need to check residuals
emmeans(cox_ad_rouge, pairwise~Traitement, infer=TRUE, type="response")$emmeans
Traitement response SE df asymp.LCL asymp.UCL null z.ratio p.value
Control 1.36 0.0832 Inf 1.204 1.531 1 4.986 <.0001
Permethrin_rouge 0.69 0.0513 Inf 0.597 0.799 1 -4.986 <.0001
Confidence level used: 0.95
Intervals are back-transformed from the log scale
Tests are performed on the log scale
$contrasts
contrast ratio SE df asymp.LCL asymp.UCL null z.ratio
Control / Permethrin_rouge 1.97 0.267 Inf 1.51 2.57 1 4.986
p.value
<.0001
Confidence level used: 0.95
Intervals are back-transformed from the log scale
Tests are performed on the log scale emmeans(cox_ad_orange, pairwise~Traitement, infer=TRUE, type="response")$emmeans
Traitement response SE df asymp.LCL asymp.UCL null z.ratio p.value
Control 1.177 0.0814 Inf 1.028 1.348 1 2.357 0.0184
Permethrin_orange 0.844 0.0607 Inf 0.733 0.972 1 -2.357 0.0184
Confidence level used: 0.95
Intervals are back-transformed from the log scale
Tests are performed on the log scale
$contrasts
contrast ratio SE df asymp.LCL asymp.UCL null z.ratio
Control / Permethrin_orange 1.39 0.197 Inf 1.06 1.84 1 2.357
p.value
0.0184
Confidence level used: 0.95
Intervals are back-transformed from the log scale
Tests are performed on the log scale